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Brain Tumor Classification Using Principal Component Analysis and Artificial Neural Network

Abdalla, Sheroug A., Msc; Mustafa, Zeinab A., PhD; Abrahim, Banazier A., PhD

Journal of Clinical Engineering: April/June 2019 - Volume 44 - Issue 2 - p 70–75
doi: 10.1097/JCE.0000000000000333
FEATURE ARTICLES
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The abnormal growth of cells in the brain is known as brain tumor. A brain tumor is a kind of disease that can hit children, adults, and older adults. In this work, a proposed method for brain tumor detection and classification using MATLAB and based on magnetic resonance imaging plays an essential role in the brain-tumor disease diagnostic application that is based on manual and automatic detection. Moreover, various kinds of tumors exist so it is complicated to detect, and thus it is hard to make decisions. Correct segmentation and image enhancement give an accurate classification of brain tumor types. A probabilistic neural network was applied for classification. Two steps were used for making the correct decision: first is feature extraction based on principal component analysis, and second is the classification done using a probabilistic neural network. The known classifications are “normal,” “benign,” and “malignant.”

Corresponding author: Zeinab A. Mustafa, PhD, Biomedical Engineering Department, College of Engineering, Sudan University of Sciences and Technology, Alamarat 61 St, PO Box 407, Khartoum, Sudan. He can be reached at (zenab42000@yahoo.com).

Sheroug A. Abdalla, MSc, is a master student, Embedded System Department, College of Engineering, Alneelan University, Khartoum, Sudan.

Zeinab A. Mustafa, PhD, is an assistant professor in the Biomedical Engineering Department, College of Engineering, Sudan University of Sciences and Technology, Khartoum, Sudan.

Banazier A. Abrahim, PhD, is an assistant professor in the Biomedical Engineering Department, College of Engineering, Sudan University of Sciences and Technology, Khartoum, Sudan.

The authors declare no conflicts of interest.

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